Computational Tools for the Analysis and Redesign of Microbial Production
Systems
by Prof. Costas D. Maranas
Details at http://www.castdiv.org/WebCAST.htm
<http://www.castdiv.org/WebCAST.htm>
Deadline to Register- 10 May 2006
DATE: Friday, 12 May 2006, 10am-12noon Pacific [1pm-3pm Eastern]
Dial-in from the comfort of your office to hear the presentation
Abstract
In this talk we will describe how optimization-based computational tools can
be used to guide strain redesign leading to targeted overproductions. For
example, production of bio-ethanol or complex molecules such as terpenes.
Using as a starting point stoichiometric models of microbial metabolism, we
will first explore how optimization can be used to pinpoint which new
functionalities to add to the microbial host to endow it with new
capabilities extracted from a generated database of more than 5,700
reactions. Building on this computational infrastructure, we will then
present an integrated framework for identifying optimal microbial strain
redesign strategies allowing for (i) additions, (ii) deletions and (iii)
modulations (i.e., activations or inhibitions) of targeted reactions in the
metabolic network. Finally we will explore how optimization can be used to
analyze the topological properties of metabolic networks, identify pathway
gaps and suggest ways of filling them. The developed computational tools
will be highlighted using a number of design case-studies and the
predictions will be contrasted with experimental results.
Biographical Sketch
Costas D. Maranas (b. 1967), Donald B. Broughton Professor, Department of
Chemical Engineering, The Pennsylvania State University, BS, Chemical
Engineering, Aristotle University, Greece, (1990); MA, Chemical Engineering,
Princeton University (1992); Ph.D. in Chemical Engineering, Princeton
University (1995); Allan P. Colburn Award for Excellence in Publication
(2002), Editorial Board for Biophysical Journal, Computers & Chemical
Engineering, Journal of Global Optimization and Metabolic Engineering;
Reviewer for NSF, NIH and DOE; Research interests: Modeling and optimization
of directed evolution protocols for protein engineering, analysis and
optimization of metabolic and signaling networks, optimal design of
biological circuits and synthetic biology, inference of gene regulatory
networks, real options based optimization of product and R&D pipelines,
optimization theory and algorithms.
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